Note: This page has been translated by MathWorks. Please click here

To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

Solve linear system of equations using Levinson-Durbin recursion

Math Functions / Matrices and Linear Algebra / Linear System Solvers

`dspsolvers`

The Levinson-Durbin block solves the *n*th-order
system of linear equations

*Ra* = *b*

in the cases where:

*R*is a Hermitian, positive-definite, Toeplitz matrix.*b*is identical to the first column of*R*shifted by one element and with the opposite sign.

$$\left[\begin{array}{cccc}r(1)& {r}^{*}(2)& \cdots & {r}^{*}(n)\\ r(2)& r(1)& \cdots & {r}^{*}(n-1)\\ \vdots & \vdots & \ddots & \vdots \\ r(n)& r(n-1)& \cdots & r(1)\end{array}\right]\text{}\left[\begin{array}{c}a(2)\\ a(3)\\ \vdots \\ a(n+1)\end{array}\right]=\left[\begin{array}{c}-r(2)\\ -r(3)\\ \vdots \\ -r(n+1)\end{array}\right]$$

The input to the block, *r* = `[`

,
can be a vector or a matrix. If the input is a matrix, the block treats
each column as an independent channel and solves it separately. Each
channel of the input contains lags *r*(1) *r*(2)
... *r*(*n*+1)]*0* through *n* of
an autocorrelation sequence, which appear in the matrix *R*.

The block can output the polynomial coefficients, *A*,
the reflection coefficients, *K*, and the prediction
error power, *P*, in various combinations. The **Output(s)** parameter
allows you to enable the *A* and *K* outputs
by selecting one of the following settings:

`A`

— For each channel, port A outputs*A*=`[1`

, the solution to the Levinson-Durbin equation.*a*(2)*a*(3) ...*a*(*n*+1)]*A*has the same dimension as the input. You can also view the elements of each output channel as the coefficients of an*n*th-order autoregressive (AR) process.`K`

— For each channel, port K outputs*K*=`[`

, which contains*k*(1)*k*(2) ...*k*(*n*)]*n*reflection coefficients and has the same dimension as the input, less one element. A scalar input channel causes an error when you select`K`

. You can use reflection coefficients to realize a lattice representation of the AR process described later in this page.`A and K`

— The block outputs both representations at their respective ports. A scalar input channel causes an error when you select`A and K`

.

Select the **Output prediction error power (P)** check
box to output the prediction error power for each channel, *P*.
For each channel, *P* represents the power of the
output of an FIR filter with taps *A* and input autocorrelation
described by *r*, where *A* represents
a prediction error filter and *r* is the input to
the block. In this case, *A* is a whitening filter. *P* has
one element per input channel.

When you select the **If the value of lag 0 is zero,
A=[1 zeros], K=[zeros], P=0** check box (default), an input
channel whose *r*`(1)`

element is
zero generates a zero-valued output. When you clear this check box,
an input with *r*`(1)`

= `0`

generates `NaN`

s in the output. In general, an input
with *r*`(1)`

= `0`

is
invalid because it does not construct a positive-definite matrix *R*.
Often, however, blocks receive zero-valued inputs at the start of
a simulation. The check box allows you to avoid propagating `NaN`

s
during this period.

One application of the Levinson-Durbin formulation implemented
by this block is in the Yule-Walker AR problem, which concerns modeling
an unknown system as an autoregressive process. You would model such
a process as the output of an all-pole IIR filter with white Gaussian
noise input. In the Yule-Walker problem, the use of the signal's autocorrelation sequence to obtain an optimal estimate
leads to an *R**a *= *b* equation
of the type shown above, which is most efficiently solved by Levinson-Durbin
recursion. In this case, the input to the block represents the autocorrelation
sequence, with `r(1)`

being the zero-lag value. The
output at the block's A port then contains the coefficients of the
autoregressive process that optimally models the system. The coefficients
are ordered in descending powers of *z*, and the
AR process is minimum phase. The prediction error, *G*,
defines the gain for the unknown system, where $$G=\sqrt{P}$$:

$$H\left(z\right)=\frac{G}{A(z)}=\frac{G}{1+a(2){z}^{-1}+\text{}\mathrm{...}\text{}+a(n+1){z}^{-n}}$$

The output at the block's K port contains the corresponding
reflection coefficients, `[`

*k*`(1) `

*k*```
(2)
...
```

*k*`(n)]`

, for the
lattice realization of this IIR filter. The Yule-Walker AR Estimator
block implements this autocorrelation-based method for AR model estimation,
while the Yule-Walker Method block extends the method to spectral
estimation.

Another common application of the Levinson-Durbin algorithm
is in linear predictive coding, which is concerned with finding the
coefficients of a moving average (MA) process (or FIR filter) that predicts the next value of a signal from
the current signal sample and a finite number of past samples. In
this case, the input to the block represents the signal's autocorrelation
sequence, with *r*`(1)`

being the
zero-lag value, and the output at the block's A port contains the
coefficients of the predictive MA process (in descending powers of *z*).

$$H\left(z\right)=A\left(z\right)=1+a(2){z}^{-1}+\text{}\mathrm{...}\text{}a(n+1){z}^{-n}$$

These coefficients solve the following optimization problem:

$$\stackrel{\mathrm{min}}{\left\{{a}_{i}\right\}}$$

$$E\left[{\left|{x}_{n}-{\displaystyle \sum _{i=1}^{N}{a}_{i}{x}_{n-i}}\right|}^{2}\right]$$

Again, the output at the block's K port contains the corresponding
reflection coefficients, `[k(1) k(2) ... k(n)]`

,
for the lattice realization of this FIR filter. The Autocorrelation
LPC block in the Linear Prediction library implements this autocorrelation-based
prediction method.

The diagrams in this section show the data types used within the Levinson-Durbin block for fixed-point signals.

After initialization the block performs *n* updates.
At the (*j*+1) update,

$$\text{valueinaccumulator}=r(j+1)+{\displaystyle \sum {a}_{j}(i)\times r\left(j-i+1\right)}$$

The following diagram displays the fixed-point data types used in this calculation:

The block then updates the reflection coefficients *K* according
to

$${K}_{j+1}=\text{valueinaccumulator}/{P}_{j}$$

The block then updates the prediction error power *P* according
to

$${P}_{j+1}={P}_{j}-{P}_{j}\times {K}_{j+1}\times \text{conj}\left({K}_{j+1}\right)$$

The next diagram displays the fixed-point data types used in this calculation:

The polynomial coefficients *A* are then updated
according to

$${a}_{j+1}(i)={a}_{j}(i)+{K}_{j+1}\times \text{conj}\left({\text{a}}_{\text{j}}(j-1+i)\right)$$

This diagram displays the fixed-point data types used in this calculation:

The algorithm requires *O(n ^{2})* operations
for each input channel. This implementation is therefore much more
efficient for large

**Main Tab**

**Output(s)**Specify the solution representation of

*R**a*=*b*to output: model coefficients (`A`

), reflection coefficients (`K`

), or both (`A and K`

). When the input is a scalar or row vector, you must set this parameter to`A`

.**Output prediction error power (P)**Select to output the prediction error at port P.

**If the value of lag 0 is zero, A=[1 zeros], K=[zeros], P=0**When you select this check box and the first element of the input,

`r(1)`

, is zero, the block outputs the following vectors, as appropriate:`A = [1 zeros(1,n)]`

`K = [zeros(1,n)]`

`P = 0`

When you clear this check box, the block outputs a vector of

`NaN`

s for each channel whose`r(1)`

element is zero.

**Data Types Tab**

Floating-point inheritance takes precedence over the data type settings defined on this pane. When inputs are floating point, the block ignores these settings, and all internal data types are floating point.

**Rounding mode**Select the rounding mode for fixed-point operations.

**Saturate on integer overflow**Select the overflow mode for fixed-point operations.

**Product output data type**Specify the product output data type. See Fixed-Point Data Types and Multiplication Data Types for illustrations depicting the use of the product output data type in this block. You can set it to:

A rule that inherits a data type, for example,

`Inherit: Same as input`

An expression that evaluates to a valid data type, for example,

`fixdt(1,16,0)`

Click the

**Show data type assistant**button to display the**Data Type Assistant**, which helps you set the**Product output data type**parameter.See Specify Data Types Using Data Type Assistant (Simulink) for more information.

**Accumulator data type**Specify the accumulator data type. See Fixed-Point Data Types for illustrations depicting the use of the accumulator data type in this block. You can set it to:

A rule that inherits a data type, for example,

`Inherit: Same as input`

An expression that evaluates to a valid data type, for example,

`fixdt(1,16,0)`

Click the

**Show data type assistant**button to display the**Data Type Assistant**, which helps you set the**Accumulator data type**parameter.See Specify Data Types Using Data Type Assistant (Simulink) for more information.

**Polynomial coefficients (A)**Specify the polynomial coefficients (

*A*) data type. See Fixed-Point Data Types for illustrations depicting the use of the A data type in this block. You can set it to an expression that evaluates to a valid data type, for example,`fixdt(1,16,15)`

.Click the

**Show data type assistant**button to display the**Data Type Assistant**, which helps you set the**A**parameter.See Specify Data Types Using Data Type Assistant (Simulink) for more information.

**Reflection coefficients (K)**Specify the polynomial coefficients (

*A*) data type. See Fixed-Point Data Types for illustrations depicting the use of the K data type in this block. You can set it to an expression that evaluates to a valid data type, for example,`fixdt(1,16,15)`

.Click the

**Show data type assistant**button to display the**Data Type Assistant**, which helps you set the**K**parameter.See Specify Data Types Using Data Type Assistant (Simulink) for more information.

**Prediction error power (P)**Specify the prediction error power (

*P*) data type. See Fixed-Point Data Types for illustrations depicting the use of the P data type in this block. You can set it to:A rule that inherits a data type, for example,

`Inherit: Same as input`

An expression that evaluates to a valid data type, for example,

`fixdt(1,16,0)`

Click the

**Show data type assistant**button to display the**Data Type Assistant**, which helps you set the**P**parameter.See Specify Data Types Using Data Type Assistant (Simulink) for more information.

**Minimum**Specify the minimum values that the polynomial coefficients, reflection coefficients, or prediction error power should have. The default value is

`[]`

(unspecified).Simulink^{®}software uses this value to perform:Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink))

Automatic scaling of fixed-point data types

**Maximum**Specify the maximum values that the polynomial coefficients, reflection coefficients, or prediction error power should have. The default value is

`[]`

(unspecified). Simulink software uses this value to perform:Parameter range checking (see Specify Minimum and Maximum Values for Block Parameters (Simulink))

Automatic scaling of fixed-point data types

**Lock data type settings against changes by the fixed-point tools**Select this parameter to prevent the fixed-point tools from overriding the data types you specify on the block mask.

Golub, G. H. and C. F. Van Loan. Sect. 4.7 in *Matrix
Computations*. 3rd ed. Baltimore, MD: Johns Hopkins University
Press, 1996.

Ljung, L. *System Identification: Theory for the User*.
Englewood Cliffs, NJ: Prentice Hall, 1987. Pgs. 278–280.

Kay, Steven M. *Modern Spectral Estimation: Theory
and Application*. Englewood Cliffs, NJ: Prentice Hall,
1988.

Double-precision floating point

Single-precision floating point

Fixed point (signed only)

8-, 16-, and 32-bit signed integers

Cholesky Solver | DSP System Toolbox |

LDL Solver | DSP System Toolbox |

Autocorrelation LPC | DSP System Toolbox |

LU Solver | DSP System Toolbox |

QR Solver | DSP System Toolbox |

Yule-Walker AR Estimator | DSP System Toolbox |

Yule-Walker Method | DSP System Toolbox |

`levinson` | Signal Processing Toolbox |

See Linear System Solvers for related information.

Was this topic helpful?